Translating Arabic Literary Metaphors: A Comparative Evaluation of ChatGPT-4.0 and Google Translate 2024 - Ohod Faisal Ahmed; Ida Kusuma Dewi; Mohammed Yunus Anis (IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 2025)
This research conducted a comparative evaluation of ChatGPT-4.0 and Google Translate 2024 in translating Arabic literary metaphors into English. We applied a qualitative research approach focusing on the novel "Memory in the Flesh" by Ahlam Mosteghanemi, the study analyzed metaphor types 27 out of 77 data, translation strategies employed by each tool, and the quality of translations based on accuracy, acceptability, and readability. The study identified the types of metaphors used in the novel, analyzed the translation strategies employed by each tool based on (Newmark, 2022) framework, and evaluated the output quality using (Nababan et al., 2012, n.d.) assessment model of accuracy, acceptability, and readability. Findings indicated that while Google Translate 2024 showed a slight edge in literal accuracy which achieved 2.85, in acceptability Google Translate 2024 achieved 2.62 score and in readability achieved 2.31, ChatGPT-4.0 significantly outperformed it in acceptability and readability crucial for literary texts, In acceptability ChatGPT4.0 achieved 2.98 and in readability 2.99 score. The study highlighted the evolving capabilities of machine translation in handling nuanced literary language and underscores the continued importance of human oversight in achieving high-quality literary. The research emphasized Involving capabilities of neural machine translation systems, particularly in handling complex and nuanced literary content. However, it also underscored their limitations, especially in capturing cultural subtleties and maintaining the figurative resonance of literary metaphors. It underscored the continued necessity of human oversight to ensure fidelity and artistic integrity in literary translation, offering insights for translators, educators, and MT developers alike.